Using Hierarchical Hidden Markov Models to Perform Sequence-Based Classification of Protein Structure

摘要：

In the post-genome era, as an essential alternative of experimental method, the computational method is becoming popular. The prediction of protein structural class from protein sequence becomes one of researchs concerns because the knowledge of protein structural class can simplify and accelerate in the computational determination of the spatial structure of a newly identified protein. As one of sequence-based approaches, hidden Markov model(HMM) provides a convenient and effective tool to analyze and classify protein sequence. In this paper, we firstly present the 6-state HMM which holds fewer states, clear transition groups and fewer model parameters. Then, by considering the knowledge of hierarchical structure of protein based on the 6-state HMM, we further propose the hierarchical hidden Markov model (HHMM) which has not only clear biological meaning, but also fewer number of transitions. Finally, the experimental comparison of various methods demonstrates that both the HHMM and the 6-state HMM outperform other method.

关键词： Protein sequence hidden Markov model hierarchical hidden Markov model Classification

作者: Jian-Yu Shi Yan-Ning Zhang

作者单位: School of Life Science School of Computer Science and Technology, Northwestern Polytechnical University, Xi’An City, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 1789-1792

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）